Matches in SemOpenAlex for { <https://semopenalex.org/work/W4246274310> ?p ?o ?g. }
Showing items 1 to 60 of
60
with 100 items per page.
- W4246274310 endingPage "164" @default.
- W4246274310 startingPage "153" @default.
- W4246274310 abstract "This chapter reviews several of the more popular regression techniques in machine learning, along with some techniques for assessing how well they performed. It presents the script that uses a dataset describing physiological measurements taken from 442 diabetes patients, with the target variable being an indicator of the progression of their disease. The simplest example of regression is one that we probably saw in high school: fitting a line to data. The standard way to fit a line is called least squares. The main ways to measure goodness-of-fit in regression situations are R2 and correlation between predictions and targets. Another way to quantify goodness-of-fit is to simply take the correlation between our predicted values and the known values in the test data. This has the advantage that we can use Pearson, Spearman, or Kendall correlation, depending on how we want to deal with outliers." @default.
- W4246274310 created "2022-05-12" @default.
- W4246274310 date "2017-01-27" @default.
- W4246274310 modified "2023-09-27" @default.
- W4246274310 title "Regression" @default.
- W4246274310 doi "https://doi.org/10.1002/9781119092919.ch11" @default.
- W4246274310 hasPublicationYear "2017" @default.
- W4246274310 type Work @default.
- W4246274310 citedByCount "0" @default.
- W4246274310 crossrefType "other" @default.
- W4246274310 hasConcept C105795698 @default.
- W4246274310 hasConcept C117220453 @default.
- W4246274310 hasConcept C119857082 @default.
- W4246274310 hasConcept C120068334 @default.
- W4246274310 hasConcept C132480984 @default.
- W4246274310 hasConcept C152877465 @default.
- W4246274310 hasConcept C154945302 @default.
- W4246274310 hasConcept C22354355 @default.
- W4246274310 hasConcept C2524010 @default.
- W4246274310 hasConcept C33923547 @default.
- W4246274310 hasConcept C41008148 @default.
- W4246274310 hasConcept C48921125 @default.
- W4246274310 hasConcept C55078378 @default.
- W4246274310 hasConcept C57381214 @default.
- W4246274310 hasConcept C79337645 @default.
- W4246274310 hasConcept C83546350 @default.
- W4246274310 hasConceptScore W4246274310C105795698 @default.
- W4246274310 hasConceptScore W4246274310C117220453 @default.
- W4246274310 hasConceptScore W4246274310C119857082 @default.
- W4246274310 hasConceptScore W4246274310C120068334 @default.
- W4246274310 hasConceptScore W4246274310C132480984 @default.
- W4246274310 hasConceptScore W4246274310C152877465 @default.
- W4246274310 hasConceptScore W4246274310C154945302 @default.
- W4246274310 hasConceptScore W4246274310C22354355 @default.
- W4246274310 hasConceptScore W4246274310C2524010 @default.
- W4246274310 hasConceptScore W4246274310C33923547 @default.
- W4246274310 hasConceptScore W4246274310C41008148 @default.
- W4246274310 hasConceptScore W4246274310C48921125 @default.
- W4246274310 hasConceptScore W4246274310C55078378 @default.
- W4246274310 hasConceptScore W4246274310C57381214 @default.
- W4246274310 hasConceptScore W4246274310C79337645 @default.
- W4246274310 hasConceptScore W4246274310C83546350 @default.
- W4246274310 hasLocation W42462743101 @default.
- W4246274310 hasOpenAccess W4246274310 @default.
- W4246274310 hasPrimaryLocation W42462743101 @default.
- W4246274310 hasRelatedWork W1980913430 @default.
- W4246274310 hasRelatedWork W2018697919 @default.
- W4246274310 hasRelatedWork W2149098201 @default.
- W4246274310 hasRelatedWork W2325374573 @default.
- W4246274310 hasRelatedWork W2375721435 @default.
- W4246274310 hasRelatedWork W2508231025 @default.
- W4246274310 hasRelatedWork W3155296579 @default.
- W4246274310 hasRelatedWork W4210660526 @default.
- W4246274310 hasRelatedWork W4249094282 @default.
- W4246274310 hasRelatedWork W4252743528 @default.
- W4246274310 isParatext "false" @default.
- W4246274310 isRetracted "false" @default.
- W4246274310 workType "other" @default.